This course will build on students' previous foundations in probability, statistical inference, and linear regression. An introduction to generalized linear models (GLMs) and multilevel (mixed effects/hierarchical) models will be followed by additional advanced topics at the discretion of the instructor. These will include special cases of GLMs and multilevel models and may also consider measurement of latent variables (e.g. factor analysis, IRT).

This course covers various approaches for network inference and delves into the following questions: How do networks we observe emerge? Under what conditions do they change (or not)? What are the network outcomes individuals get based on their structural positions and roles? How do information and resources move from one spot to another within and beyond networks? Where does the flow stop? What are the mechanisms that lead to changes to networks?

This course provides an introduction to statistical methods for analyzing temporal data, including time-series analysis, panel data modeling, and event history analysis. It equips students with the skills to model and interpret data that evolves over time and includes techniques for causal inference in temporal settings. Students will begin with foundational methods in time-series analysis, including descriptive tools, autoregressive integrated moving average (ARIMA) models, and autoregressive distributed lag (ADL) models.

How can social scientists convey data through narrative and reports geared toward general audiences or specific stakeholders? How can they convey those data through visuals geared toward non-scientists? This hands-on course provides students with the knowledge and skills needed to generate strong, data-driven communication.

This course serves as a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master?s students with a data analytics or computational social science focus. The material covered will include a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power).

This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research. An emphasis is placed on measurement reliability and validity, internal research design validity, and generalizability, or external research design validity.

The course provides an overview of the general linear regression model ? one of the most widely used inferential tools in the social sciences. This course first focuses on the model and its statistical properties. We will then consider generalizations or extensions of the model that have been designed to handle violations of the basic model?s assumptions. Topics typically include the general linear model, hypothesis testing, nonlinearities in variables, interactions, diagnostics, heteroscedastic residuals, limited dependent variables, measurement error, and causal inference.

The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This is a one-credit seminar which may be taken repeatedly for credit. May be repeated for credit up to six times.

Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The two credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship.